Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters

Abdul Syukor, Mohamad Jaya and Mu*ath Ibrahim, Mohammad Jarrah and Mohd Asyadi Azam, Mohd Abid and Mohd Razali, Muhamad (2016) Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters. Journal Of Theoretical And Applied Information Technology, 84. pp. 355-369. ISSN 1992-8645

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Optimization of thin film coating parameters is important in identifying the required output.Two main issues of the process of physical vapor deposition (PVD) are manufacturing costs and customization of cutting tool properties.The aim of this study is to identify optimal PVD coating process parameters.Three process parameters were selected,namely nitrogen gas pressure (N2),argon gas pressure (Ar),and Turntable Speed (TT),while thin film grain size of titanium nitrite (TiN) was selected as an output response.Coating grain size was characterized using Atomic Force Microscopy (AFM) equipment.In this paper,to obtain a proper output result,an approach in modeling surface grain size of Titanium Nitrite (TiN)coating using Response Surface Method (RSM) has been implemented. Additionally,analysis of variance (ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters,genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.

Item Type: Article
Uncontrolled Keywords: TiN, Grain Size, Modeling, Sputtering, PVD, RSM, PSO.
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Faculty of Manufacturing Engineering > Department of Engineering Materials
Depositing User: Mohd. Nazir Taib
Date Deposited: 22 May 2018 06:15
Last Modified: 10 Jul 2021 16:54
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